Cluster AML-ETO SO
TLusardi
11/04/2020
####Operations
* Cluster each object individually * Crudely identify clusters in the DMSO object * Transfer cluster identities to the ava, ory, and combo objects
#####Data Import Options - File naming importDate - select the creation date for the desired object Filtering - build in ability to process differently filtered objects in parallel + nofilt: objects include all cells that pass min/max counts, max % mitochondria + filtered: objects filtered to have similar numbers of cells, counts/cell
#####Regression Options (in each object) * regress - start from preprocessed objects + noregress + regress.CC - regress on cell cycle - probably not the best option as it removes any cell cycle information that may be important for differentiating cells + regress.diff - regress on the difference in cell cycle values; per Seurat notations, it preserves cell cycle information relevant to development
Set up libraries and directories
Cluster individual experiments
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
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## Number of nodes: 1657
## Number of edges: 52048
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8081
## Number of communities: 11
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
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## Number of nodes: 1657
## Number of edges: 52895
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8044
## Number of communities: 9
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 1657
## Number of edges: 52344
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8082
## Number of communities: 9
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
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## Number of nodes: 2740
## Number of edges: 87365
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8156
## Number of communities: 9
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 2740
## Number of edges: 87636
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8135
## Number of communities: 10
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 2740
## Number of edges: 87553
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8152
## Number of communities: 10
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
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## Number of nodes: 3379
## Number of edges: 109379
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8181
## Number of communities: 11
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 3379
## Number of edges: 109935
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8124
## Number of communities: 11
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 3379
## Number of edges: 110141
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8163
## Number of communities: 11
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
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## Number of nodes: 1859
## Number of edges: 60017
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7847
## Number of communities: 9
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 1859
## Number of edges: 60607
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7802
## Number of communities: 9
## Elapsed time: 0 seconds
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
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## Number of nodes: 1859
## Number of edges: 60263
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## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.7841
## Number of communities: 9
## Elapsed time: 0 secondsPlot Individual Clusters
###Sankey Plots to illustrate clustering differences
Identify dmso Clusters (loosely!)
CD34/SOX4/ERG are stem cell genes one end of the dog bone
CD14, ITGAX, LYZ are mature monocyte genes at the other end of the dog bone
Note: Will need to redo this in filtered objects
## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.
Assign DMSO cluster names
Transfer Cluster Labels
## Performing PCA on the provided reference using 2170 features as input.## Projecting PCA## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...## Warning in DietSeurat(object = query, features = features): No features found in
## assay 'ADT', removing...## Finding neighborhoods## Finding anchors## Found 4977 anchors## Filtering anchors## Retained 4721 anchors## Finding integration vectors## Finding integration vector weights## Predicting cell labels## Performing PCA on the provided reference using 2142 features as input.## Projecting PCA## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...
## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...## Finding neighborhoods## Finding anchors## Found 5238 anchors## Filtering anchors## Retained 4904 anchors## Finding integration vectors## Finding integration vector weights## Predicting cell labels## Performing PCA on the provided reference using 2079 features as input.## Projecting PCA## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...
## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...## Finding neighborhoods## Finding anchors## Found 4034 anchors## Filtering anchors## Retained 3936 anchors## Finding integration vectors## Finding integration vector weights## Predicting cell labels## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.cc_ to sctcc_## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.cc_ to sctcc_## Performing PCA on the provided reference using 2170 features as input.## Projecting PCA## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...## Warning in DietSeurat(object = query, features = features): No features found in
## assay 'ADT', removing...## Finding neighborhoods## Finding anchors## Found 4833 anchors## Filtering anchors## Retained 4584 anchors## Finding integration vectors## Finding integration vector weights## Predicting cell labels## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.cc_ to sctcc_## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.cc_ to sctcc_## Performing PCA on the provided reference using 2142 features as input.## Projecting PCA## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...## Warning in DietSeurat(object = query, features = features): No features found in
## assay 'ADT', removing...## Finding neighborhoods## Finding anchors## Found 5142 anchors## Filtering anchors## Retained 4836 anchors## Finding integration vectors## Finding integration vector weights## Predicting cell labels## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.cc_ to sctcc_## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.cc_ to sctcc_## Performing PCA on the provided reference using 2079 features as input.## Projecting PCA## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...## Warning in DietSeurat(object = query, features = features): No features found in
## assay 'ADT', removing...## Finding neighborhoods## Finding anchors## Found 3948 anchors## Filtering anchors## Retained 3836 anchors## Finding integration vectors## Finding integration vector weights## Predicting cell labels## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.diff_ to sctdiff_## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.diff_ to sctdiff_## Performing PCA on the provided reference using 2170 features as input.## Projecting PCA## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...## Warning in DietSeurat(object = query, features = features): No features found in
## assay 'ADT', removing...## Finding neighborhoods## Finding anchors## Found 4956 anchors## Filtering anchors## Retained 4747 anchors## Finding integration vectors## Finding integration vector weights## Predicting cell labels## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.diff_ to sctdiff_## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.diff_ to sctdiff_## Performing PCA on the provided reference using 2142 features as input.## Projecting PCA## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...## Warning in DietSeurat(object = query, features = features): No features found in
## assay 'ADT', removing...## Finding neighborhoods## Finding anchors## Found 5183 anchors## Filtering anchors## Retained 4831 anchors## Finding integration vectors## Finding integration vector weights## Predicting cell labels## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.diff_ to sctdiff_## Warning: Keys should be one or more alphanumeric characters followed by an
## underscore, setting key from sct.diff_ to sctdiff_## Performing PCA on the provided reference using 2079 features as input.## Projecting PCA## Warning in DietSeurat(object = reference, features = features): No features
## found in assay 'ADT', removing...## Warning in DietSeurat(object = query, features = features): No features found in
## assay 'ADT', removing...## Finding neighborhoods## Finding anchors## Found 3996 anchors## Filtering anchors## Retained 3887 anchors## Finding integration vectors## Finding integration vector weights## Predicting cell labels
Compare transferred vs. native clusters
Save the data
## [1] "nofilt objects: Saving scaled/normalized/regressed data in individual objects in aml_eto.indivClustSO.nofilt.2020-11-04.rds"## R version 4.0.2 (2020-06-22)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dplyr_1.0.2 patchwork_1.0.1 networkD3_0.4 knitr_1.30
## [5] ggplot2_3.3.2 data.table_1.13.0 Seurat_3.2.2
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-149 matrixStats_0.57.0 RcppAnnoy_0.0.16
## [4] RColorBrewer_1.1-2 httr_1.4.2 sctransform_0.3
## [7] tools_4.0.2 R6_2.4.1 irlba_2.3.3
## [10] rpart_4.1-15 KernSmooth_2.23-17 uwot_0.1.8
## [13] mgcv_1.8-33 lazyeval_0.2.2 colorspace_1.4-1
## [16] withr_2.3.0 tidyselect_1.1.0 gridExtra_2.3
## [19] compiler_4.0.2 plotly_4.9.2.1 labeling_0.3
## [22] scales_1.1.1 lmtest_0.9-38 spatstat.data_1.4-3
## [25] ggridges_0.5.2 pbapply_1.4-3 spatstat_1.64-1
## [28] goftest_1.2-2 stringr_1.4.0 digest_0.6.25
## [31] spatstat.utils_1.17-0 rmarkdown_2.4 pkgconfig_2.0.3
## [34] htmltools_0.5.0 fastmap_1.0.1 htmlwidgets_1.5.2
## [37] rlang_0.4.7 shiny_1.5.0 farver_2.0.3
## [40] generics_0.0.2 zoo_1.8-8 jsonlite_1.7.1
## [43] ica_1.0-2 magrittr_1.5 Matrix_1.2-18
## [46] Rcpp_1.0.5 munsell_0.5.0 abind_1.4-5
## [49] reticulate_1.16 lifecycle_0.2.0 stringi_1.5.3
## [52] yaml_2.2.1 MASS_7.3-53 Rtsne_0.15
## [55] plyr_1.8.6 grid_4.0.2 parallel_4.0.2
## [58] listenv_0.8.0 promises_1.1.1 ggrepel_0.8.2
## [61] crayon_1.3.4 deldir_0.1-29 miniUI_0.1.1.1
## [64] lattice_0.20-41 cowplot_1.1.0 splines_4.0.2
## [67] tensor_1.5 pillar_1.4.6 igraph_1.2.5
## [70] future.apply_1.6.0 reshape2_1.4.4 codetools_0.2-16
## [73] leiden_0.3.3 glue_1.4.2 evaluate_0.14
## [76] vctrs_0.3.4 png_0.1-7 httpuv_1.5.4
## [79] gtable_0.3.0 RANN_2.6.1 purrr_0.3.4
## [82] polyclip_1.10-0 tidyr_1.1.2 future_1.19.1
## [85] xfun_0.18 rsvd_1.0.3 mime_0.9
## [88] xtable_1.8-4 later_1.1.0.1 survival_3.2-7
## [91] viridisLite_0.3.0 tibble_3.0.3 cluster_2.1.0
## [94] globals_0.13.0 fitdistrplus_1.1-1 ellipsis_0.3.1
## [97] ROCR_1.0-11